Abstract. Many cyclone detection and tracking methods (CDTMs) have been developed in the past to study the climatology of extratropical cyclones. However, all CDTMs have different approaches in defining and tracking cyclone centers. This naturally leads to cyclone track climatologies of inconsistent physical characteristics. More than that, it is typical for CDTMs to produce a non-negligible amount of bogus tracks which can be perceived as “false positives”, or more generally as CDTM artifacts, i.e. tracks of weak atmospheric features that do not correspond to large or mesoscale vortices. Lack of consensus in CDTM outputs and the inclusion of significant amounts of bogus tracks therein, has long prohibited the production of a commonly accepted reference dataset of extratropical cyclone tracks. Such a dataset could allow comparable results on the analysis of storm track climatologies and could also contribute to the evaluation and improvement of CDTMs. To cover this gap, we present a new methodological approach that combines overlapping tracks from different CDTMs and produces composite tracks that concentrate the agreement of more than one CDTM. In this study we apply this methodology to the outputs of 10 well-established CDTMs which were originally applied to ERA5 reanalysis in the 42-year period of 1979–2020. We tested the sensitivity of our results to the spatio-temporal criteria that identify overlapping cyclone tracks, and for benchmarking reasons, we produced five reference datasets of subjectively tracked cyclones. Results show that climatological numbers of composite tracks are substantially lower than the ones of individual CDTM, while benchmarking scores remain high (i.e. counting the number of subjectively tracked cyclones captured by the composite tracks). This suggests that our method is able to filter out a large portion of bogus tracks. Indeed, our results show that composite tracks tend to describe more intense and longer-lasting cyclones with more distinguished early, mature and decay stages than the cyclone tracks produced by individual CDTMs. Ranking the composite tracks according to their confidence level (defined by the number of contributing CDTMs), it is shown that the higher the confidence level, the more intense and long-lasting cyclones are produced. Given the advantage of our methodology in producing cyclone tracks with physically meaningful, distinctive life stages and including a minimum number of bogus tracks, we propose composite tracks as reference datasets for climatological research in the Mediterranean. The supplementary material provides the composite Mediterranean tracks for all confidence levels and in the conclusion we discuss their adequate use for scientific research and applications.

Atmospheric rivers are important atmospheric features implicated in the global water vapor budget, the cloud distribution, and the associated precipitation. The ARiD (Atmospheric River Detector) code has been developed to automatically detect atmospheric rivers from water vapor flux and has been applied to the ECMWF ERA5 archive over the period 1980–2020 above the Atlantic Ocean and Europe. A case study of an atmospheric river formed in the East Atlantic on August 2014 that reached France has been detailed using ECMWF ERA5 reanalysis, ground based observation data, and satellite products such as DARDAR, AIRS, GPCP, and GOES. This atmospheric river event presents a strong interaction with an intense upper tropospheric jet stream, which induced stratosphere–troposphere exchanges by tropopause fold. A 1980–2020 climatology of atmospheric rivers over Europe has been presented. The west of France, Iberian Peninsula, and British Islands are the most impacted regions by atmospheric rivers with an occurrence of up to four days per month during the October–April period. Up to 40% of the precipitation observed on the west European coast can be linked to the presence of ARs. No significant trend in the occurrence of the phenomena was found over 1980–2020.

<p>Cyclones are essential elements of the climate and of the water cycle in the Mediterranean. The most intense of them lead to natural disasters because of their violent winds and extreme rainfall, which can cause significant damage to the territories bordering the Mediterranean (coast and mountain ranges). Reliable forecasts of cyclones are therefore essential to better anticipate and prevent their societal impact. However, their predictability is often limited by their particularities: smaller cyclones with a shorter life cycle than in the North Atlantic, complex topography, interactions with the relatively warm sea and air masses laden with dust from the Sahara.</p><p>We investigate the predictability of Mediterranean cyclones in a systematic framework using an ensemble prediction system. A reference dataset was first obtained by tracking cyclones in the ERA5 reanalysis (1979-2021), using an algorithm developped for the North Atlantic and adapted for the Mediterranean region. We then investigated the predictability using ARPEGE ensemble reforecasts in a homogeneous configuration over 22 years (2000-2021).</p><p>We restricted the study on 500 cases, which were selected using a storm severity index based on wind gusts and adapted for the Mediterranean region. The cases were then divided in several categories following their dynamical context, their intensity and their geographical origin. The predictability of the reforecasts was finally quantified on each of those categories, using probabilistic scores on cyclone trajectories (along and cross track error) and on intensities (mean sea level pressure and storm severity index).</p><p>While past studies have been limited by the fact that regular updates of operational forecasting systems do not allow the predictability of cases to be compared with each other, the homogeneous configuration of the ARPEGE ensemble reforecasts makes it possible to systematically identify the limitation to the predictability of Mediterranean cyclones.</p>